Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC

2018/07/03 by Reihaneh Entezari, Entezari, Reihaneh, Patrick E. Brown +3
Mathematics · #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #stat.AP #stat.CO #stat.ME

paper · pdf · doi:10.48550/arxiv.1807.01239

arxiv created 2018/07/03 · arxiv updated 2018/07/04

Abstract

In this paper, we perform Bayesian Inference to analyze spatial tree count data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian Generalized Linear Geostatistical Model and implement a Markov Chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training data is reduced is studied and compared with a Logistic Regression model without a spatial effect. Finally, we discuss a stratified sampling approach for selecting subsets of data that allows for potential better predictions.

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